Create gradio_call.py

gradio_call.py

@@ -0,0 +1,10 @@
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+
+tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+model = GPT2LMHeadModel.from_pretrained("./results")  # Load from the output directory of your training script
+
+def generate_text(prompt):
+    inputs = tokenizer.encode(prompt, return_tensors="pt")
+    outputs = model.generate(inputs, max_length=150, num_return_sequences=1, no_repeat_ngram_size=2)
+    generated = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return generated